Significantly wetter or drier future conditions for one to two thirds of the world’s population

Future projections of precipitation are uncertain, hampering effective climate adaptation strategies globally. Our understanding of changes across multiple climate model simulations under a warmer climate is limited by this lack of coherence across models. Here, we address this challenge introducing an approach that detects agreement in drier and wetter conditions by evaluating continuous 120-year time-series with trends, across 146 Global Climate Model (GCM) runs and two elevated greenhouse gas (GHG) emissions scenarios. We show the hotspots of future drier and wetter conditions, including regions already experiencing water scarcity or excess. These patterns are projected to impact a significant portion of the global population, with approximately 3 billion people (38% of the world’s current population) affected under an intermediate emissions scenario and 5 billion people (66% of the world population) under a high emissions scenario by the century’s end (or 35-61% using projections of future population). We undertake a country- and state-level analysis quantifying the population exposed to significant changes in precipitation regimes, offering a robust framework for assessing multiple climate projections.

The goal of this arficle is to assess populafions that will be subject to wet or dry trends.While the topic is impacfful, conclusions at country level are generalized and may lead to misinterpretafion of results, an undesirable outcome as the topic is of great importance and the 'takeaway messages' need to be accurately delivered, as such papers can have broad impacts.Thus, I urge authors to carefully consider the way they are conveying their messages and be careful in providing figures that will not lead to misinterpretafion and further confusion.I will separate these major issues here a) comparisons at country level Nafions considered in this discussion have different territory and the distribufion of the populafion vary significantly within these territories.Results presented in the document and supplemental figures clearly show that agreements are not observed over large areas (this is common sense among the science community) and generalizafions can lead to false interpretafion.Take for example South America and the annual agreement (for simplificafion).The 'wefting' agreement (S2) is more consistent over subtropical south America (affecfing Southern Brazil, Uruguay, NE Argenfina and subtropical Atlanfic Ocean), whereas the 'dry agreement' is observed over Northern Amazon and adjacent countries.Likewise, North America (Canada, the U.S.) show a clear seasonality in the signal of the agreement with large spafial variability (different seasons revert the signal of agreement in some parts of the U.S. and Canada).Thus, I understand that the idea of Figure 1 is to show a summary of these findings, but the generalizafions at country level are not realisfic, and may lead to undesired misinterpretafions.For instance, Brazil appears at the 40-50% drying agreement whereas only Uruguay shows high agreement (although not catalogued with the bar-plots).The drying agreement was mostly consistent (over seasons) in the Amazon and parts of Northeast Brazil -which might sfill be resulfing from the poor representafion of the Atlanfic ITCZ, a problem that does not seem to have improved with the CMIP6 generafion.Perhaps authors should comment on that too, as they likely have examined seasonal precipitafion fields in these models.The quesfion is, how decision makers, the media and the readers of this arficle will interpret Figure-1 plots?Essenfially, that future scenarios of emissions should lead to enhanced droughts in Brazil, Southern and Northern Africa, and enhanced wefting condifions in Canada and the U.S. (to cite the large countries that are discussed here) These assumpfions are misleading and can have serious consequences!If a country's budget is designed to help mifigafing the effects of climate change, results presented in an arficle in Nature can seriously influence how decision makers will plan to deal with the climate crisis and future disasters.Canada has been a good example in 2023, and there is liftle doubt that we could aftribute JJA fire season to the expected drying trends shown in JJA in Fig. S7 in the present arficle.Decision-makers and the media will focus on Fig. 1, and probably ignore S7.In summary, maps that appear in Figure 1 should be replaced with maps that are more realisfic (with the appropriate regionalizafion of the results (as, for instance, shown in Figure 2).
b) The issue with the populafion affected by future emissions.I may have missed some important informafion about how the populafion was accounted for in this study, but it was not clear in the provided reference (Tatem, World Pop) and respecfive links whether any projecfions of populafion change was considered here.It seems that populafion was esfimated based on the 2021 census for most countries.The quesfion is, are populafions assumed to be constant over fime?What was used to esfimate future projecfions?This does not appear in the arficle (or the supplemental material.Thus, this needs further clarificafion or at least a caveat indicafing that these numbers can change Regardless of the methodology to account for the trend in populafion, again, it is unclear how the number "4,350,912,136' people was esfimated (how this aggregafion was done at grid level, since the methodology to esfimate trends was performed at grid level).How sure are we that this number (which is very precise in terms of digits) is not under (or over) esfimafing the expected affected populafion?Of parficular concern is Figure 2 "warming agreement" where circles represent the affected populafion.Are these circles proporfional to the enfire populafion of a given country or the populafion effecfively affected by model agreements?This needs to be properly discussed in the figure capfions (bubble size shows populafion…).c) Comments on the literature review and introducfion: "Climate modes such as El Niño-Southern Oscillafion, Indian Ocean Dipole, Southern Annular Mode, and Atlanfic Mulfidecadal Oscillafion contribute to precipitafion variability".The oversimplificafion here needs aftenfion.It is understandable that not all possible coupled modes of variability that influence precipitafion can be discussed here, but the way the sentence is wriften makes the non-expert reader believe that these are the only exisfing modes modulafing precipitafion.Why the Southern Annular mode and not the Northern Annular mode, or why the PNA, the PSA, the NAO and PDO are not menfioned here?I suggest that this sentence be re-wriften to indicate that there are modes that vary from interannual (actually from intraseasonal) to mulfi-decadal fime scales that influence precipitafion, and cite a few of them to exemplify."Future projecfions for climate modes do not align over fime across GCMs, resulfing in highly heterogeneous projecfions ( ref 6,7)".This sentence seems to imply that, because climate modes (say El Nino) do not align over fime across GCMs, there is disagreement among models.This is obviously not correct when considering long periods (the reason why we normally consider more than one decade).Moreover, this is not said in any of the two cited references.So, please rephrase or remove this sentence from the manuscript.

General Evaluafion:
This manuscript aims to advance our understanding of how the confinuous increase of greenhouse-gases (GHG) might affect global precipitafion regimes and furthermore impact global populafions.An ensemble of 146 CMIP5/6 model simulafions under intermediate and high emission scenarios is applied to detect agreement in wefter and drier condifions across the world and more interesfingly connect precipitafion change to exposed populafion at the country scale.There are already many regions experiencing precipitafion regime change at both wet and dry end.This research indicates that this trend will confinue and more and more people would be affected by the end of this century.The manuscript is well-organized, and the results are very interesfing and have great significance in the field.In parficular, the country-scale analysis offers a good framework for pracfical applicafion such as designing/making climate adapfion policies for different regions.I suggest it be accepted for publicafion in the current format.
Minor typos: 1) Line 227, "county" should be "country" 2) Line 244, add "is" before "worth"?The goal of this article is to assess populations that will be subject to wet or dry trends.While the topic is impactful, conclusions at country level are generalized and may lead to misinterpretation of results, an undesirable outcome as the topic is of great importance and the 'takeaway messages' need to be accurately delivered, as such papers can have broad impacts.Thus, I urge authors to carefully consider the way they are conveying their messages and be careful in providing figures that will not lead to misinterpretation and further confusion.I will separate these major issues here Thank you very much for the time allocated to review our material and for your in-depth assessment.The valuable points raised certainly contributed to improve the quality of our material.
We carefully thought about the way we presented our regionalizations and improved the analysis to account for intracountry variability of model agreement, which we detail below on topic R1.2.

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R1.2 a) comparisons at country level Nations considered in this discussion have different territory and the distribution of the population vary significantly within these territories.Results presented in the document and supplemental figures clearly show that agreements are not observed over large areas (this is common sense among the science community) and generalizations can lead to false interpretation.Take for example We think this is a valid point, which we carefully addressed using a three-tier strategy to reduce generalizations as follow: 1) Inclusion of spatial variability lines (denoting 10 th and 90 th spatial percentiles per country) over Figure 1 bar plots showing country-level model agreement; DONE South America and the annual agreement (for simplification).The 'wetting' agreement (S2) is more consistent over subtropical south America (affecting Southern Brazil, Uruguay, NE Argentina and subtropical Atlantic Ocean), whereas the 'dry agreement' is observed over Northern Amazon and adjacent countries.Likewise, North America (Canada, the U.S.) show a clear seasonality in the signal of the agreement with large spatial variability (different seasons revert the signal of agreement in some parts of the U.S. and Canada).Thus, I understand that the idea of Figure 1 is to show a summary of these findings, but the generalizations at country level are not realistic, and may lead to undesired misinterpretations.For instance, Brazil appears at the 40-50% drying agreement whereas only Uruguay shows high agreement (although not catalogued with the bar-plots).The drying agreement was mostly consistent (over seasons) in the Amazon and parts of Northeast Brazil -which might still be resulting from the poor representation of the Atlantic ITCZ, a problem that does not seem to have improved with the CMIP6 generation.Perhaps authors should comment on that too, as they likely have examined seasonal precipitation fields in these models.
2) Calculations of regional statistics of model agreement and population exposure across all 3752 states globally and inclusion as supplementary dataset; 3) Country-level maps were replaced by state-level maps to show intra-country spatial variability yet aggregated across jurisdictions to facilitate decision making.We also included detailed map insets to better illustrate regions with elevated internal spatial variability and sharp gradients.
In addition, we included a paragraph discussing the matter and encouraging readers to refer to the gridded dataset for refined spatial scales insights and interpretations as follow: "Importantly, for some countries such as the United States, Brazil, Chile, Indonesia and South Africa with heterogeneous spatial patterns and/or notable internal gradients of wetting and drying agreement, spatial means may be too generalized to inform policies (see lines over Figure 1 bar charts representing spatial variability).Therefore, for regions with high internal heterogeneity as well as for information on more refined spatial scales, decision-makers are recommended to refer to the state level regionalizations instead (Figure 1 (maps) and Table SX) as well as the gridded dataset." Uruguay has also been included in the bar plots.
We have also added the following sentence in the discussion: "Model deficiencies such as ITCZ biases [56][57][58] might also be affecting the drying patterns across specific regions -e.g., the Amazon and parts of Northeast Brazil." R1.3 The question is, how decision makers, the media and the readers of this article will interpret Figure-1 plots?Essentially, that future scenarios of emissions should lead to enhanced droughts in Brazil, Southern and Northern Africa, and enhanced wetting conditions in Canada and the U.S. (to cite the large countries that are discussed here) These assumptions are misleading and can have serious consequences!If a country's budget is designed to help mitigating the effects of climate change, results presented in an article in Nature can seriously influence how decision makers will plan to deal with the climate crisis and future disasters.Canada has been a good example in 2023, and there is little doubt that we could attribute JJA fire season to the expected drying trends shown in JJA in Fig. S7 in the present article.Decision-makers and the media will focus on Fig. 1, and probably ignore S7.In summary, maps that appear in Figure 1 should be replaced with maps that are more realistic (with the appropriate regionalization of the results (as, for instance, shown in Figure 2).
Figure 1 has been redesigned to address these points following detailed response on topic R1.2 and new state-level regionalizations have been presented as maps and included as supplementary data with more realistic regionalizations for decisionmakers.
Figure S7 (now Figure S8 in our revised version) shows differences in drying and wetting agreement from intermediate to very highemission scenarios -that is [RCP8.5 and SSP5-8.5]-[RCP4.5 and SSP2-4.5]for annual and seasonal rainfall.It is insightful but its interpretation is complex, and we thought it could confuse the general public.That's why we decided to include it as a supplementary figure.

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R1.4 b) The issue with the population affected by future emissions.I may have missed some important information about how the population was accounted for in this study, but it was not clear in the Thanks, this is another important point that we are happy to be provided with the opportunity to address.

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provided reference (Tatem, World Pop) and respective links whether any projections of population change was considered here.It seems that population was estimated based on the 2021 census for most countries.The question is, are populations assumed to be constant over time?What was used to estimate future projections?This does not appear in the article (or the supplemental material.Thus, this needs further clarification or at least a caveat indicating that these numbers can change In this revised version of our manuscript, we have also estimated the future affected population using downscaled population projections for moderate and high emission scenarios (KC & Lutz 2017; Wang et al 2022).
We have included in the text global statistics for future population under different emissions.A supplementary figure repeating Figure 2 bubble plots with estimated affected populations by country using future population was included as well.
"When future population projections are considered instead, (3,26 billion people) and 65.6% (5,22 billion people) of the 2100 world's population are projected to be affected by wetter or drier conditions under moderate and very high emissions respectively.The distribution of future affected population across countries is consistent with our current population estimates (Figure S3)." In addition, we produced regionalised statistics including both current and projected future population at both country-and state-levels to be released as supplementary dataset with the goal of facilitating climate adaptation and decision-making globally.
R1.5 Regardless of the methodology to account for the trend in population, again, it is unclear how the number "4,350,912,136' people was estimated (how this aggregation was done at grid level, since the methodology to estimate trends was performed at grid level).How sure are we that this number (which is very precise in terms of digits) is not under (or over) estimating the expected affected population?
We have quantified the current and future affected population by overlaying the gridded population datasets over the wetting and drying regions with agreement greater than 50% (majority threshold) for moderate and very-high emissions showed on Figure 2 map with shades and contours for very high and intermediate emissions respectively.We have clarified it in the Methods as follow: "To estimate the global population affected, the 1km grid cells falling within the wetting and drying agreement masks are summed.To estimate the country-and state-scale affected populations, the grid cells falling within the agreement and country masks are summed." We have rounded the population numbers to billions and millions as it is more appropriate as global scale information.The bubble size on Figure 2 (and Figure S3) display the affected current (and future) population by country following the approach described above on topic R1.2, rather than the entire populations.In some countries such as Bangladesh, India, Portugal and Spain, the entire population is projected to be affected by these patterns under very high emissions.
We have extended the description referring to bubble size and explicitly stated "affected population" on legend title to make the This manuscript aims to advance our understanding of how the continuous increase of greenhouse-gases (GHG) might affect global precipitation regimes and furthermore impact global populations.An ensemble of 146 CMIP5/6 model simulations under intermediate and high emission scenarios is applied to detect agreement in wetter and drier conditions across the world and more interestingly connect precipitation change to exposed population at the country scale.There are already many regions experiencing precipitation regime change at both wet and dry end.This research indicates that this trend will continue and more and more people would be affected by the end of this century.The manuscript is well-organized, and the results are very interesting and have great significance in the field.In particular, the country-scale analysis offers a good framework for practical application such as designing/making climate adaption policies for different regions.I suggest it be accepted for publication in the current format.
Thank you very much for such fantastic feedback.We are delighted to hear your opinion about our research and very excited by the opportunity to have our manuscript under consideration for publication in Nature Communications.

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Of particular concern is Figure2"warming agreement" where circles represent the affected population.Are these circles proportional to the entire population of a given country or the population effectively affected by model agreements?This needs to be properly discussed in the figure captions (bubble size shows population…).
RESPONSE TO REVIEWERS' COMMENTS ON NATURE COMMUNICATIONS MANUSCRIPT NCOMMS-23-26096"Significantly wetter or drier future conditions for one to two thirds of the world's population" generations).The main goal is to provide a country-level analysis quantifying the population exposed to significant changes in precipitation regimes.The methodology considers time-series from 1980-2099 and investigates trends at model grid level.Conclusions are based on percent of agreement among models on wet or dry trends.The statistical significance is assessed by non-parametric Mann-Kendell tests, and trends are estimated with the Theil-Sen Slope to estimate the trend magnitude.
Why the Southern Annular mode and not the Northern Annular mode, or why the PNA, the PSA, the NAO and PDO are not mentioned here?I suggest that this sentence be rewritten to indicate that there are modes that vary from interannual (actually from intraseasonal) to multi-decadal time scales that influence precipitation, and cite a few of them to exemplify.R1.8 "Future projections for climate modes do not align over time acrossGCMs, resulting in highly heterogeneous projections ( ref 6,7)".This sentence seems to imply that, because climate modes (say El Nino) do not align over time across GCMs, there is disagreement among models.This is obviously not correct when considering long periods (the reason why we normally consider more than one decade).Moreover, this is not said in any of the two cited references.So, please rephrase or remove this sentence from the manuscript.Thank you for pointing out this potential point of confusion.What we meant here was to refer to multiple climate modes acting simultaneously rather than only one.We have rewritten the sentence, which now reads:"Future projections of multiple climate modes, their interactions and resultant teleconnections with precipitation do not align over time across GCMs, resulting in highly heterogeneous projections" Reviewer #2 R2.1 General Evaluation: